Problem: high-dimensional time series forecasting
?? what is "high-dimensional" time series forecasting?
one dimension for each individual time-series. n个time series为n维。
A need for exploiting global pattern and coupling them with local calibration校准 for better prediction.
However, most are one-dimensional forecasting.
one-dimensional forecasting VS high-dimensional forecasting:
1. a single dimension forecast mainly depends on past values from the same dimension.
DeepGLO: a deep forecasting model which thinks globally and acts locally.
A hybrid model: a global matrix factorization model regularized by a temporal convolution network + a temporal network that capture local properties of each time-series and associated covariates相关协变量.
Environment: different time series can have vastly different scales without a priori normalization or rescaling.
Introduction:
需求：比如零售商，one may be interested in the future daily demands for all items in a category. This leads to a problem of forecasting n time-series.
Traditional methods: focus on one time-series or a small number of time-series at a time.
AR, ARIMA, exponential smoothing and so on.
?? how to share temporal patterns in the whole data-set while training and prediction?
RNN - sequential modeling; and suffer from the gradient vanishing/ exploding problems.
LSTM 解决了上述问题。
Wavenet model: temporal convolutions/ causal convolutions.
Temporal convolution has been recently used, however, they still have two important shortcomings:
1. hard to train on data-sets that have wide variation in scales.
2. even though these deep models are trained on the entire data-set, during prediction the models only focus on local past data. i.e only the past data of a time-series is used for predicting the future of that time-series.
global properties. take in multiple time-series in the input layer thus capturing global properties.